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Künstliche Intelligenz in der Neurochirurgie

Artificial intelligence in neurosurgery

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Zusammenfassung

Hintergrund

Künstliche Intelligenz (KI) in der Neurochirurgie erlebt mit dem Voranschreiten technologischer Entwicklungen einen zunehmenden Stellenwert. Abzulesen ist diese Entwicklung an der Zunahme von Publikationen zum Thema KI in der Neurochirurgie in den letzten Jahren.

Ziel der Arbeit

Mit der vorliegenden Übersicht soll ein Einblick in die aktuellen Möglichkeiten des Einsatzes von KI in der Neurochirurgie gegeben werden.

Material und Methoden

Sichtung der Literatur mit Fokus auf exemplarische Arbeiten beim Einsatz von KI in der Neurochirurgie.

Ergebnisse

Die aktuellen neurochirurgischen Arbeiten zum Einsatz von KI zeigen die Mannigfaltigkeit der Thematik im Fachgebiet. Haupteinsatzgebiete stellen hierbei Diagnose‑, Prognose- und Outcomemodelle dar.

Diskussion

Die unterschiedlichen Einsatzgebiete der KI im Bereich der Neurochirurgie mit einer verfeinerten präoperativen Diagnostik und Outcomevorhersage werden die Zukunft der Neurochirurgie maßgeblich beeinflussen. Der Neurochirurg wird zwar weiterhin die Operationsindikationen stellen, aber eine optimierte Aussage zum Operationsrisiko und Operationserfolg werden die Neurochirurgen zukünftig mithilfe von KI vornehmen.

Abstract

Background

Artificial intelligence (AI) in neurosurgery is becoming increasingly more important as the technology advances. This development can be measured by the increase of publications on AI in neurosurgery over the last years.

Objective

This article provides insights into the current possibilities of using AI in neurosurgery.

Material and methods

A review of the literature was carried out with a focus on exemplary work on the use of AI in neurosurgery.

Results

The current neurosurgical publications on the use of AI show the diversity of the topic in this field. The main areas of application are diagnostics, outcome and treatment models.

Conclusion

The various areas of application of AI in the field of neurosurgery with a refined preoperative diagnostics and outcome predictions will significantly influence the future of neurosurgery. Neurosurgeons will continue to make the decisions on the indications for surgery but an optimized statement on diagnosis, treatment options and on the risk of surgery will be made by neurosurgeons with the help of AI in the future.

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Literatur

  1. 1.

    Arle JE, Perrine K, Devinsky O, Doyle WK (1999) Neural network analysis of preoperative variables and outcome in epilepsy surgery. J Neurosurg 90:998–1004. https://doi.org/10.3171/jns.1999.90.6.0998

  2. 2.

    Azimi P (2014) Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis. J Neurosurg. https://doi.org/10.3171/2013.12.PEDS13423

  3. 3.

    Azimi P (2014) Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis. J Neurosurg Spine 20:298–299. https://doi.org/10.3171/2013.10.SPINE13851

  4. 4.

    Azimi P, Mohammadi HR, Benzel EC et al (2015) Use of artificial neural networks to predict recurrent lumbar disk herniation. J Spinal Disord Tech 28:E161–5. https://doi.org/10.1097/BSD.0000000000000200

  5. 5.

    Baumgarten C, Zhao Y, Sauleau P et al (2016) Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation. J Med Imaging 3:25001–25009. https://doi.org/10.1117/1.JMI.3.2.025001

  6. 6.

    Buchlak QD, Esmaili N, Leveque J‑C et al (2019) Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev. https://doi.org/10.1007/s10143-019-01163-8

  7. 7.

    Celtikci E (2017) A systematic review on machine learning in neurosurgery: the future of decision making in patient care. Turk Neurosurg. https://doi.org/10.5137/1019-5149.JTN.20059-17.1

  8. 8.

    Cohen KB, Glass B, Greiner HM et al (2016) Methodological issues in predicting pediatric epilepsy surgery candidates through natural language processing and machine learning. Biomed Inform Insights 8:38308–38308. https://doi.org/10.4137/BII.S38308

  9. 9.

    Devin CJ, Bydon M, Alvi MA et al (2018) A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis from the quality outcomes database. Neurosurg Focus 45:E9–10. https://doi.org/10.3171/2018.8.FOCUS18326

  10. 10.

    Dumont TM, Rughani AI, Tranmer BI (2011) Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg 75:57–63. https://doi.org/10.1016/j.wneu.2010.07.007

  11. 11.

    Emblem KE, Due-Tonnessen P, Hald JK et al (2013) Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI. J Magn Reson Imaging 40:47–54. https://doi.org/10.1002/jmri.24390

  12. 12.

    Galbusera F, Casaroli G, Bassani T (2019) Artificial intelligence and machine learning in spine research. JOR Spine 2:e1044–21. https://doi.org/10.1002/jsp2.1044

  13. 13.

    Habibi Z, Ertiaei A, Nikdad MS et al (2016) Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network. Childs Nerv Syst. https://doi.org/10.1007/s00381-016-3248-2

  14. 14.

    Hale AT, Stonko DP, Wang L et al (2018) Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging. Neurosurg Focus 45:E4–6. https://doi.org/10.3171/2018.8.FOCUS18191

  15. 15.

    Khor S, Lavallee D, Cizik AM et al (2018) Development and validation of a prediction model for pain and functional outcomes after lumbar spine surgery. JAMA Surg 153:634–639. https://doi.org/10.1001/jamasurg.2018.0072

  16. 16.

    Kim JS, Merrill RK, Arvind V et al (2018) Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine 43:853–860. https://doi.org/10.1097/BRS.0000000000002442

  17. 17.

    Macyszyn L, Akbari H, Pisapia JM et al (2016) Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol 18:417–425. https://doi.org/10.1093/neuonc/nov127

  18. 18.

    Nelson DW, Nyström H, MacCallum RM et al (2010) Extended analysis of early computed tomography scans of traumatic brain injured patients and relations to outcome. J Neurotrauma 27:51–64. https://doi.org/10.1089/neu.2009.0986

  19. 19.

    Oermann EK, Kress M‑A, Collins BT et al (2013) Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery 72:944–952. https://doi.org/10.1227/NEU.0b013e31828ea04b

  20. 20.

    Park A, Chute C, Rajpurkar P et al (2019) Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model. JAMA Netw Open 2:e195600–12. https://doi.org/10.1001/jamanetworkopen.2019.5600

  21. 21.

    Rughani AI, Dumont TM, Lu Z et al (2010) Use of an artificial neural network to predict head injury outcome. J Neurosurg 113:585–590. https://doi.org/10.3171/2009.11.JNS09857

  22. 22.

    Senders JT, Staples PC, Karhade AV et al (2017) Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. https://doi.org/10.1016/j.wneu.2017.09.149

  23. 23.

    Shi H‑Y, Hwang S‑L, Lee K‑T, Lin C‑L (2013) In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. J Neurosurg 118:746–752. https://doi.org/10.3171/2013.1.JNS121130

  24. 24.

    Staartjes VE, Serra C, Muscas G et al (2018) Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurg Focus 45:E12–7. https://doi.org/10.3171/2018.8.FOCUS18243

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Correspondence to M. M. Bonsanto.

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Interessenkonflikt

M.M. Bonsanto und V. Tronnier geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Bonsanto, M.M., Tronnier, V.M. Künstliche Intelligenz in der Neurochirurgie. Chirurg (2020). https://doi.org/10.1007/s00104-020-01131-9

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Schlüsselwörter

  • Tumorchirurgie
  • Neurovaskuläre Erkrankungen
  • Outcomevorhersagen
  • Diagnosevorhersagen
  • Therapievorhersagen

Keywords

  • Tumor surgery
  • Neurovascular diseases
  • Outcome predictions
  • Diagnostic predictions
  • Treatment predictions